Heart Rate Variability (HRV) as a Tool for Diagnostic and Monitoring Performance in Sport and ...

29 Pages·2013·0.53 MB·English

by  Sharon Griffin

103

Journal of Exercise Physiologyonline

June 2013
Volume 16 Number 3

JEPonline
EOdffiitcoira-li nR-eCsheieafr ch Journal of
Totmhem Ay mBoeorincea,n P ShoDc, iMetByA o f
ReEvixeewrc Bisoea rPdh ysiologists
Todd Astorino, PhD
Heart Rate Variability (HRV) as a Tool for Diagnostic
Julien BIaSkSeNr ,1 P09h7D- 9751
Steve Brock, Ph D and Monitoring Performance in Sport and Physical
Lance Dalleck, PhD
Activities
Eric Goulet, PhD
Robert Gotshall, PhD
Alexander Hutchison, PhD Bojan Makivić¹, Marina Djordjević Nikić2, Monte S. Willis3
M. Knight-Maloney, PhD
Len Kravitz, PhD 1Center for Sport Science and University Sport, University of Vienna,
James Laskin, PhD
Auf der Schmelz 6, 1150 Vienna, Austria, 2Faculty of Sport and
Yit Aun Lim, PhD
Lonnie Lowery, PhD Physical Education, University of Belgrade, Blagoja Parovića 156,
Derek Marks, PhD 11000 Belgrade, Serbia, 3McAllister Heart Institute, University of North
Cristine Mermier, PhD Carolina, 2336 Medical Biomolecular Research Building, Campus Box
Robert Robergs, PhD
# 7525, NC 27599 Chapel Hill, USA
Chantal Vella, PhD

Dale Wagner, PhD
Frank Wyatt, PhD ABSTRACT
Ben Zhou, PhD Makivić B, Djordjević Nikić M, Willis MS. Heart Rate Variability
(HRV) as a Tool for Diagnostic and Monitoring Performance in Sport

and Physical Activities. JEPonline 2013;16(3):103-131. The dynamic

autonomic responses during exercise can be measured to give

Official Research Journal actionable information for training by analysis of the ECG to determine
of the American Society of heart rate variability (HRV). While application of HRV has been
Exercise Physiologists applied to predict sudden cardiac death and diabetic neuropathy in

assessing disease progression, recent studies have suggested that it
ISSN 1097-9751
may be applied to exercise training. In this review, we present the

rationale for measuring HRV. We describe the different variables used
and what they can tell us about the autonomic nervous system. The
use of HRV in detecting changes in exercise intensity is presented,
along with evidence that gender and age changes may affect
autonomic HRV. Lastly, we illustrate how HRV measurements taken
immediately post-exercise have proven to be useful in measuring and
monitoring training load to proscribe workouts and/or prevent over-
training. Despite the studies that vary widely in their application to
different training levels of athletes being tested and HRV measures
used, the standardization of methodologies and results should help
accelerate the use of HRV in sports training.

Key Words: Autonomic Nervous System; Overtraining; HRV
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TABLE OF CONTENTS

ABSTRACT------------------------------------------------------------------------------------------------------------ 103
1. INTRODUCTION ------------------------------------------------------------------------------------------------- 105
1.1 ORIGINS OF HEART RATE VARIABILITY (HRV) MEASUREMENTS------------------ 105
1.2 BASICS OF THE AUTONOMIC NERVOUS SYSTEM AND ITS REGULATION
OF HRV ------------------------------------------------------------------------------------------------- 105
1.3 COMMONLY USED HRV PARAMETERS/DESCRIPTORS ------------------------------ 107
1.4 APPLICATION OF HRV ON EXERCISE ------------------------------------------------------- 108
1.5 EXPERIMENTAL APPROACH TO THE PROBLEM ---------------------------------------- 109
2. RESULTS ---------------------------------------------------------------------------------------------------------- 109
2.1 BASICS OF HEART RATE VARIABILITY (HRV)-----------------------------------------------110
2.2 MEASUREMENT OF HRV DURING DIFFERENT EXERCISE INTENSITIES --------110
3. ASSOCIATION OF EXERCISE, GENDER, AND AGE WITH AUTONOMIC TONE ------------113
4. HRV MEASUREMENT TAKEN POST-EXERCISE: WHAT THEY CAN TELL US ABOUT
EXERCISE JUST COMPLETED ----------------------------------------------------------------------------- 114
4.1 HRV TAKEN IMMEDIATELY POST-EXERCISE DURING RECOVERY REFLECTS
THE EFFECTS OF THE EXERCISE ON THE BODY --------------------------------------- 114
4.2 EXERCISE LOAD DISTRIBUTIONS ARE REFLECTED BY HRV POST-EXERCISE114
4.3 TRAINED ATHLETES HAVE DIFFERENT HRV POST-EXERCISE THAN LESS
TRAINED ATHLETES ------------------------------------------------------------------------------- 114
4.4 HIGH FREQUENCY POWER (HFP) (VAGAL REACTIVATION) AFTER EXERCISE
IS BLUNTED WITH EXERCISE INCREASED EXERCISE INTENSITY IN
SEDENTARY WOMEN ------------------------------------------------------------------------------115
4.5 POST-EXERCISE, LFP, AND TP ARE INCREASED WITH INCREASE WORKLOAD
IN MALE ENDURANCE ATHLETES -------------------------------------------------------------115
5. HRV AS A MONITORING TOOL IN ATHLETIC TRAINING PROGRAMS------------------------- 118
6. HRV, OVERTRAINING, AND OVER-REACHING STATES ------------------------------------------- 121
7. DISCUSSION ------------------------------------------------------------------------------------------------------ 125
8. SUMMARY---------------------------------------------------------------------------------------------------------- 126
9. REFERENCES------------------------------------------------------------------------------------------------ 127
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1. INTRODUCTION

The way in which the cardiovascular system responds to the stress of exercise has captured the
imagination of sports physiologists for the past century (5). Critical adjustments are continually made
to the cardiovascular system to meet the diverse demands of the musculature and heart (16,51).
These dynamic adjustments in cardiac and peripheral vascular control, including their regulation by
the autonomic nervous system (ANS), occur in response to rapid changes in heart rate and blood
pressure. Analysis of electrocardiographic (ECG) tracings allows for the measurement of the
autonomic nervous system. The application of heart rate variability (HRV) to assess risk for sudden
cardiac death and diabetic neuropathy is well known (28,44). However, the use of HRV in monitoring
sports training is in its infancy, despite technological advances in heart rate monitors that allow the
collection of ECG tracings fairly conveniently. In this review, we present the underlying rationale for
HRV measurement as a surrogate marker of autonomic nervous system function, the commonly used
analytics applied to HRV, and present how HRV has been applied to exercise in athletes and non-
athletes alike. We then review the literature to illustrate how HRV measures change with different
exercise intensities, and describe how exercise, gender, and age changes may affect the autonomic
tone determined by HRV. Lastly, we describe the utility of taking HRV measurements immediately
post-exercise to determine training load and present how HRV may be used as a monitoring tool in
athletic training programs to avoid over-training. Despite studies that vary widely in their application to
different training levels of the athletes being tested and HRV measures used, standardization of
methodologies and the publishing of results are still needed and will help accelerate the potential use
of HRV monitoring in sports training in the future.

1.1 Origins of Heart Rate Variability (HRV) Measurements
In 1733, Stephen Hales first observed variation in arterial pressure and beat-to-beat time length
during the respiratory cycle in horses (8,9). The first documented report of variability of and cardiac
rhythms is credited to Carl Ludwig in 1847, when he documented respiratory sinus arrhythmia (RSA)
using a smoked drum kymograph, a device he invented that allowed for the measurement of
mechanical activity (8,9). Using galvanometers in the early 20th century, William Einthoven produced
the first continuous recordings of the electrical activity of the heart (8,9). In early 1960s, Norman Jeff
Holter developed a small portable device capable of obtaining long-term ambulatory ECGs (>24 hr)
(9). The device resulted in exponential growth in information regarding the relationship between HRV
and disease. Time domain analysis was performed on these early ECG measurements, whereas
frequency domain analysis did not exist until the early 1970s (9). The detection of heart rate variability
in relation to monitoring performance in physical activities is a relatively new field that detects
changes in the autonomic system in response to exercise.

1.2 Basics of the Autonomic Nervous System and Its Regulation of HRV
The sympathetic and parasympathetic (autonomic) nervous systems innervate the heart and regulate
heart rate (HR). The balance between these systems affects the consistency in the time between
heart beats; a measure determined by calculated the heart rate variability (HRV). As illustrated in
Figure 1 the HRV represents a physiological phenomenon that may be monitored and analyzed to
determine the state of the nervous system that controls the heart. A simple method of detecting the
autonomic response involves determining variations in consecutive time intervals between peaks of
the QRS complex called the R-R interval, which can be collected in relatively simple devices such as
modern wrist computers (1,37,50).
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Figure 1. Model of HRV Control. This illustrates independent actions of the vagal, α- and β- sympathetic
system. Their action can be assessed by measuring heart rate variability, blood pressure variability, and the
baroreflex mechanism. The parasympathetic nervous system is responsible for the bradycardia accompanying
baroreceptor stimulation, and for the tachycardia accompanying baroreceptor deactivation, with the
sympathetic nervous system also playing a minor role (5).

The effects of the ANS on the heart’s R-R intervals are complex, resulting from both innervation of the
heart and the vasculature. The heart and circulatory system are controlled primarily by higher brain
centers (central command) and cardiovascular control areas in the brain stem through the activity of
the ANS, which is composed of sympathetic and parasympathetic nerves. The medulla is the primary
site to regulate sympathetic and parasympathetic (vagal) outflow to the heart and blood vessels.
Specifically, the nucleus tractus solitarius in the medulla receives sensory input and stimulates
cardiovascular responses to emotion and stress, including physical stress (e.g., exercise). From the
medulla, the parasympathetic vagus nerve innervates the heart, as do sympathetic nerve fibers. The
right and left vagus nerves innervate the sinoatrial (SA) and atrioventricular (AV) nodes, respectively.
The atria are also innervated by vagal efferents, whereas the ventricular myocardium is sparsely
innervated by vagal efferents. Sympathetic efferent nerves are present throughout the atria (including
the conduction system), particularly in the SA node and ventricles.

Sympathetic stimulation increases the heart rate, contractility, and conduction velocity, whereas
parasympathetic stimulation has the opposite effect. Sympathetic effects are mediated through
adrenoreceptors (alpha and beta). Parasympathetic effects are relayed through muscarinic receptors.
Autonomic control of the cardiovascular system is also affected by baroreceptors, chemoreceptors,
muscle afferents, local tissue metabolism, and circulating hormones (5) (Figure 2). Although both the
sympathetic and parasympathetic systems are active at rest, the parasympathetic fibers release
acetylcholine, which acts to slow the pacemaker potential of the SA node and thus reduce heart rate.
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Figure 2. R-R Variations of R-R Distance, Measured Above, Make Up the Data Used to Calculate the
Heart Rate Variability (HRV). Adapted from Aubert et al. (5).

1.3 Commonly Used HRV Parameters/Descriptors
The most commonly used HRV parameters in ANS evaluation are the frequency-domain, time-
domain, and Poincaré plot parameters. While the nomenclature of the different measurements is
complex, they represent different ways to look at the variability and distribution of the heart rate over
time and parallel the similar, and unique, uses of mean, median, and mode as descriptors of
population measurements. Frequency-domain analysis describes high and low frequency rates of the
variability changes, which correspond to the activity of different branches of the ANS. By applying
these frequency range differences in HRV analysis, researchers are able to distinguish the individual
contributions of the sympathetic and parasympathetic systems (1,2). Low-frequency (LF) modulation
(0.04-0.15 Hz) of R-R interval changes corresponds to the sympathetic and parasympathetic activities
together. High-frequency (HF) modulation (0.15-0.4 Hz) of R-R interval changes is primarily regulated
through innervation of the heart through the parasympathetic (vagal) nerve. In the second HRV
parameter described here, common time-domain parameters reflect the standard deviation (SD) of all
N-N intervals (SDNN) that reflect the total variability and the root mean square of SDs between
adjacent N-N intervals (RMSSD), which reflects parasympathetic activity (1). The N-N interval here
corresponds to the R-R interval of normal (sinus) beats. In the third HRV parameter represented by
the Poincaré plot, a person’s R-R intervals are plotted over time and standard deviation is used to
interpret changes seen in the plot. The standard descriptor 1 (SD1) represents the fast beat-to-beat
variability in the R-R intervals, while the standard descriptor 2 (SD2) describes the longer-term
variability (47). The SD1 reflects mainly the parasympathetic input to the heart, while SD2 reflects
both sympathetic and parasympathetic contributions to the heart.

The HRV increases when respiratory frequency decreases. Although respiration greatly affects the
HRV, the absence of standardized models of respiratory frequency makes it difficult to interpret HRV
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data from that perspective. According to the previous studies, researchers have accepted various
respiratory frequency ranges (e.g., from 6 to 15 breaths·min-1). However, it is clear that a self-
organized respiratory pattern should be maintained during the recording period (15,42,43,49). Also, it
is important that standard protocols and methods for research with athletes should be established
with regard to exercise intensity and duration, respiration rate, position of the body during recording,
and duration of recording (5).

1.4 Application of HRV on Exercise
Based on data from major indexing sources, the number of studies that have examined heart rate
variability (HRV) in general has steadily increased at least 5-fold since 1990 (through 2012), and the
number investigating the influences of different physical activities on HRV has risen dramatically
since 1999 (Figure 3). The HRV has long been investigated for its role in cardiac health during
disease processes. However, recent studies have shown that HRV parameters change during and
after exercise. These findings suggest that the HRV parameters could be used to analyze the stress
the body experiences during training and to gain insight into physiological recovery after training. In
fact, it is reasonable that changes in the autonomic nervous system (ANS) patterns reflected by
changes in the HRV may serve as useful parameters for managing physical fatigue and exercise
intensity (3). Moreover, information regarding the extent to which the body recovers after training may
provide useful data for the personalization of sports training, training loads, and recovery times.

Figure 3. The Number of Publications Having the Key Words “Heart Rate Variability” (HRV), HRV and
Sport, Overtraining, and Physical Training has Continued to Increase Since 1990. Based on searches
made on four major biomedical and sports medicine databases (detailed in 1.5 Experimental Approach to the
Problem Section).
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In this review, we summarize how HRV analysis has been reported in sport science. We also discuss
the advantages and limitations of the applications of these studies to the training and monitoring of
the physiological performance of athletes.

1.5 Experimental Approach to the Problem
Four major biomedical and sports medicine databases (PubMed, MEDLINE, NSCA, and ACSM) were
reviewed for studies on heart rate variability and it relationship to exercise training from 1990-2011.
The 2001-2010 Proceedings of the German Union for Sport Science were also reviewed. The Union
holds annual conferences to discuss the application of HRV in sports. The key search terms were:
heart rate variability, HRV and sport, HRV and overtraining, HRV and physical activity, and HRV and
physical fatigue. Given the diversity of athlete populations studied, the methodologies and training
regimens used, and the mathematical analyses applied in these studies, meta-analysis was not
possible.

Subjects and Procedures. To be included in this review, studies had to incorporate research and
athletes, individuals who exercised regularly, or sedentary individuals challenged with exercise. Also,
the studies had to include the Institutional Review Board or Ethics Committee approval of their
projects including the appropriate informed consent.

2. RESULTS
Most athletes understand the importance of recovery after exercise, which is defined as the return of
body homeostasis after training to pre-training or near pre-training levels. Recovery involves getting
adequate rest before training continues to allow the body to repair and strengthen itself between
workouts. By allowing recovery to pre-training or near pre-training levels, recovery supports
optimizing future performance. However, according to Zatsiorsky and Karaemer (52) this near-training
level is based on the constantly changing dynamic of the athlete and the amount of further training
that is encountered. Recovery seeks to minimize the negative effects of training, such as fatigue, of
the previous workout while retaining the positive effect (i.e., physical fitness and improved
performance). To optimize recovery, monitoring after workouts is very important to avoid excessive
accumulation of physical fatigue during preparation or competition. If recovery is not achieved,
performance begins to decline, as is seen in the overtraining syndrome in which training for an event
or the event itself pushes beyond the body’s ability to recover.

Researchers in the field of sport science are intensely engaged in identifying tools to estimate the
quantity of physical stress in the training process needed to optimize competitive performance. The
goal of training is to put a sufficient training load on the body to disturb homeostasis and the whole
autonomic balance. Athletes may expect a better response in terms of physiological adaptation only if
the recovery of the body is sufficient (24). The question then becomes how to measure the recovery
level in an objective manner to maximize the benefits of training.

Traditionally, researchers studying recovery and performance level in the experimental laboratory
have assessed aerobic fitness by measuring various parameters, including the maximum oxygen
uptake (VO max), minimum velocity needed to reach VO max (vVO max), velocity at the onset of
2 2 2
blood lactate accumulation (VOBLA), and repeated sprint abilities. Other factors in the blood have
also been described, including creatine kinase, cortisol, white blood cell counts, C-reactive protein
(CRP), myeloperoxidase protein levels (MPO), and glutathione status (17,27). Although these
measures are periodically used to determine performance and recovery (12,38), they are either too
expensive or complicated for everyday use, even for professional athletes. In addition to requiring
large, expensive equipment that is found primarily in performance laboratories, exercise physiologists
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must be available to conduct the tests and analyze the results. Accordingly, scientists and coaches
are looking for useful, noninvasive methods to obtain valuable data about physiological changes that
occur in response to physical activity.

Optimal training depends on matching the specific abilities of the individual, such as muscle strength,
endurance, explosiveness, and flexibility to the individual’s aerobic capacity, training load, and
recovery. Since improvements and adjustments in each of these abilities are constantly changing with
continued training, balancing these multiple components to prevent overtraining can be challenging.
To this end, the use of heart rate variability (HRV) may be one logical solution because it reflects the
major regulatory processes after exercise. While multiple recent studies have used HRV to detect
which measures change in relationship to exercise type and intensity, its use also illustrates how
monitoring physical fitness during exercise and the post-exercise periods may be applied to athletic
training more broadly in the future (11,21).

2.1 Basics of Heart Rate Variability (HRV)
The lengths of successive R peaks (RR) in the QRS complex can be described mathematically; the
R-R parameter is not consistent between successive R peaks. How this measure varies can be
described mathematically in a number of different ways. During the onset of physical activity, R-R
intervals become shorter and more uniform (less varied), resulting from increased sympathetic activity
and parasympathetic withdraw. It is becoming increasingly appreciated that the variation in R-R
intervals can provide actionable information regarding the physiological stress and fatigue levels
during and after training. A variety of mathematical and algorithmic models that represent R-R
intervals can be used to present the data in time or frequency domains or even in non-linear methods.
Despite the complexity of the mathematics involved in these methods, wrist watch monitors such as
those made by Polar or Suunto or portable devices made by Omegawave Sport Technology Systems
can collect the data for additional analysis using widely available software. As an example, autonomic
activation may be evaluated by analyzing the HRV to estimate the sympathetic-vagal balance (41,49).
The frequency-domain analysis of HRV provides different parameters related to sympathetic activity
(low frequency/LF) or parasympathetic (vagal) activity (high frequency/HF). Both LF and HF may be
given as absolute values or ratios (LF/HF). Additionally, low frequency and high frequency
parameters of HRV may be provided as normalized values (LFn and HFn) by calculating the fraction
of LF or HF relative to the total minus very low frequency (VLF) (13). The standard deviation of the N-
N intervals (SDNN) has also been associated with changes in vagal activity (31).

2.2. Measurement of HRV during Different Exercise Intensities
The sympathovagal balance changes in response to different intensities and durations of aerobic
training, as evidenced by changes in HRV measures, including significant changes LF, HF, and total
power of the frequency domain. In studies of a recreational cyclist, changes in the HRV power
spectra were detected in an intensity-dependent manner (21) (Figure 4). Challenged with a workload
of 60 W that was increased by 20 W every 3 min, the HRV power spectra of a cyclist differed
quantitatively before and after a series of 20-min exercise pulses at various workloads. At baseline,
the normalized LF (LFn) was slightly greater than the normalized HF (HFn). As exercise intensity
increased over time, the LF/HF ratio increased with exercise intensity, reflecting an increase in the
sympathetic tone (increased LFnu) and a decrease in the parasympathetic tone (decreased HFnu).
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Figure 4. HRV Power Spectra of Recreational Athlete Before and After a Series of 20-min Exercise
Impulses at Various Workloads. Vertical axis represents power spectrum density while horizontal axis
depicts frequency of each frequency band. Circles describe relationship between Low Frequency (LFn) and
High Frequency (HFn) bands in normalized units during rest and different intensity of exercise. The area under
three different lines represent area of Total Power (TP) during rest (solid line) and different exercise intensity
(dotted lines). A: before exercise; B: at 50% dVO /dt max, HR 125 beats·min-1, lactate 1.7 mmol·L-1; C: at 80%
2
dVO /dt max, HR 155 beats·min-1, lactate 3.0 mmol·L-1). Used with permission and translated from Hottenrott
2
et al. (21).

The results of HRV spectra indicated by LFn and HFn in exercise vary between different reports. For
example, a study of the differences in HRV at different ventilatory thresholds (VTs) identified a
sympathetic predominance (indicated by an increased LF/HF ratio) during relatively less intense
exercise and parasympathetic predominance (indicated by a decreased LF/HF ratio) during relatively
more intense exercise. Cottin et al. (14) investigated 11 regional elite triathletes (age, 14.6 ± 1.1 yrs)
who were challenged with a moderate-intensity test (below VT power) or high-intensity test (above VT
power) until exhaustion. Under each circumstance, the R-R interval, VO , VCO , and blood lactate
2 2
levels were measure. They identified higher absolute LF, HF, and TP values at moderate-intensity
compared to high-intensity cycling. The normalized results indicated that the LFn was significantly
higher in cycling below VT (LF 80% ± 10% vs. HF 20% ± 10%, P<0.001), indicating predominance of
the sympathetic input (14). The opposite was seen during heavy exercise (LF 11% ± 8% vs. HF 89%
± 8%, P<0.001), which indicated predominance of the parasympathetic input (14). These results may
be explained by changes in the breathing rate combined with disappearance of the cardiac autonomic
control that occurs with heavy exercise conditions. Furthermore, the LF/HF ratio was always >1 for
cycling below VT and <1 for cycling above VT (4.92 ± 2.4 and 0.14 ± 0.12, respectively; P<0.001)
(14). This gradual switch of the LF/HF ratio implies the overstepping of the VT and could be a reliable
index of VT detection from the HRV.

Although Cottin and colleagues (14) found an HFn predominance only under conditions of high-
intensity exercise, Pichon et al. (34) reported different patterns in the HFn and LFn distribution as
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physical effort increased. Fourteen healthy-trained subjects exercised for 3, 6, and 9 min at 60% and
70% of the power achieved at PVO max and for 3 and 6 min (or 3 min twice) at 80% PVO max
2 2
(power achieved at maximal oxygen consumption). With increasing exercise intensity, the HFn
component reflecting parasympathetic modulation increased, whereas the LFn component reflecting
sympathetic modulation decreased. The authors concluded that parasympathetic respiratory control
and nonautonomic mechanisms may influence the HF-peak shift with strenuous exercise, whereas
the indices of sympathetic activity do not reflect changes in autonomic modulation with exhaustive
exercise (34).

The HRV indices change with exercise, reflecting the changes in the autonomic system that occurs
(summarized in Table 1). Recent studies illustrate that exercise and increasing exercise intensities
results in detectable changes in HRV measures, reflecting the relative input of the autonomic system
the body makes in response.

Table 1. Measurement of HRV during Different Exercise Intensities.

Publication Subjects Test HRV Other Results
(n) Protocol Variables Variables
Hottenrott et 1 Bicycle HFnu, LFnu VO max, Changes in the power
2
al., 2006 ergometer lactate, spectra of HRV were
(21) recreational HR identified with Increased
cyclist cycling intensity (50% VO
2
(male) max and 80% VO max).
2
Increased LFnu was
identified with increased
cycling intensity.

Cottin et al., 11 Bicycle TP, LF, HF, VO ,VCO2,V At moderate intensity,
2
2004 ergometer LFnu, HFnu, E, VT, lactate cycling (30% below PVT)
(14) regional LF/HF, RR compared to high intensity
(Enry, cycling (10% above PVT)
Cedex, higher values of HF, LF and
France) TP was identified. Higher
elite LFnu was seen with
triathletes moderate intensity cycling,
(9 males, whereas higher HFnu was
2 females) found at high intensity
cycling. LF/HF was always
>1 during moderate intensity
cycling, whereas during high
intensity cycling LF/HF was
<1.

Pichon et al. 14 Bicycle ergometer LF, HF, LFnu, %PVO max With increased exercise
2
2004 healthy HFnu, LF/HF intensity, increased HFnu
(34) trained and lower LFnu was
subjects identified. Increasing
(male) exercise intensity was
associated with shift HF
peak frequency related to an
increase in respiratory rate.

LF Low frequency power spectra; HF High frequency power spectra; LFnu Normalized units of low frequency power; HFnu Normalized
units of high frequency power; TP Total power of HRV power spectra; LF/HF low frequency power spectra to high frequency power
spectra ratio; HR Heart rate; VO2 max Maximal oxygen consumption; VO2 Oxygen consumption (absolute in L·min-1); VCO2 Carbon
dioxide release (absolute in L·min-1); VT Ventilatory threshold; %PVO2 max Percentage of cycling power output achieved at maximal
oxygen consumption.